SoroushVahidi/scidocs huggingface dataset:
Multibiometric Cryptosystems Based on Feature-Level Fusion
(2012)

Multibiometric systems are being increasingly de- ployed in many large-scale biometric applications (e.g., FBI-IAFIS, UIDAI system in India) because they have several advantages such as lower error rates and larger population coverage compared to unibiometric systems. However, multibiometric systems require storage of multiple biometric templates (e.g., fingerprint, iris, and face) for each user, which results in increased risk to user privacy and system security. One method to protect individual templates is to store only the secure sketch generated from the corresponding template using a biometric cryptosystem. This requires storage of multiple sketches. In this paper, we propose a feature-level fusion framework to simultaneously protect multiple templates of a user as a single secure sketch. Our main contributions include: (1) practical implementation of the proposed feature-level fusion framework using two well-known biometric cryptosystems, namery,fuzzy vault and fuzzy commitment, and (2) detailed analysis of the trade-off between matching accuracy and security in the proposed multibiometric cryptosystems based on two different databases (one real and one virtual multimodal database), each containing the three most popular biometric modalities, namely, fingerprint, iris, and face. Experimental results show that both the multibiometric cryptosystems proposed here have higher security and matching performance compared to their unibiometric counterparts.
A virtual translation technique to improve current regulator for salient-pole AC machines
(2004)

This paper presents a synchronous frame current regulator for salient-pole AC machines, which is insensitive to both parameters and synchronous frequencies. The transformation from stationary to synchronous frame is known to produce a frame-dependent cross-coupling term, which results in degradation in current response at high speeds and is represented as an asymmetric pole in the complex vector form of such systems. The asymmetric pole close to right half plane (RHP) in the complex vector s-domain cannot be perfectly canceled by parameter-based design of PI controllers due to parameter variation. Complex vector design synchronous frame PI current regulator with a virtually translated asymmetric pole is shown in this paper to reduce the parameter sensitivity and to improve disturbance rejection. The current regulator with the virtually translated pole have been analyzed in complex vector form for nonsalient-pole AC machine and in scalar form for salient-pole AC machines. It has been shown to yield comparable performances in both cases. This paper includes experimental verification of the performance of the proposed current regulator for an IPMSM.
News-based trading strategies
(2016)

The marvel of markets lies in the fact that dispersed information is instantaneously processed and used to adjust the price of goods, services and assets. Financial markets are particularly efficient when it comes to processing information; such information is typically embedded in textual news that is then interpreted by investors. Quite recently, researchers have started to automatically determine news sentiment in order to explain stock price movements. Interestingly, this so-called news sentiment works fairly well in explaining stock returns. In this paper, we design trading strategies that utilize textual news in order to obtain profits on the basis of novel information entering the market. We thus propose approaches for automated decisionmaking based on supervised and reinforcement learning. Altogether, we demonstrate how news-based data can be incorporated into an investment system.
Handcrafted features with convolutional neural networks for detection of tumor cells in histology images
(2016)

Detection of tumor nuclei in cancer histology images requires sophisticated techniques due to the irregular shape, size and chromatin texture of the tumor nuclei. Some very recently proposed methods employ deep convolutional neural networks (CNNs) to detect cells in H&E stained images. However, all such methods use some form of raw pixel intensities as input and rely on the CNN to learn the deep features. In this work, we extend a recently proposed spatially constrained CNN (SC-CNN) by proposing features that capture texture characteristics and show that although CNN produces good results on automatically learned features, it can perform better if the input consists of a combination of handcrafted features and the raw data. The handcrafted features are computed through the scattering transform which gives non-linear invariant texture features. The combination of handcrafted features with raw data produces sharp proximity maps and better detection results than the results of raw intensities with a similar kind of CNN architecture.
Inverse procedural modeling of 3D models for virtual worlds
(2016)

This course presents a collection of state-of-the-art approaches for modeling and editing of 3D models for virtual worlds, simulations, and entertainment, in addition to real-world applications. The first contribution of this course is a coherent review of inverse procedural modeling (IPM) (i.e., proceduralization of provided 3D content). We describe different formulations of the problem as well as solutions based on those formulations. We show that although the IPM framework seems under-constrained, the state-of-the-art solutions actually use simple analogies to convert the problem into a set of fundamental computer science problems, which are then solved by corresponding algorithms or optimizations. The second contribution includes a description and categorization of results and applications of the IPM frameworks. Moreover, a substantial part of the course is devoted to summarizing different domain IPM frameworks for practical content generation in modeling and animation.
Lack of sleep affects the evaluation of emotional stimuli
(2010)

Sleep deprivation (SD) negatively affects various cognitive performances, but surprisingly evidence about a specific impact of sleep loss on subjective evaluation of emotional stimuli remains sparse. In the present study, we assessed the effect of SD on the emotional rating of standardized visual stimuli selected from the International Affective Picture System. Forty university students were assigned to the sleep group (n=20), tested before and after one night of undisturbed sleep at home, or to the deprivation group, tested before and after one night of total SD. One-hundred and eighty pictures (90 test, 90 retest) were selected and categorized as pleasant, neutral and unpleasant. Participants were asked to judge their emotional reactions while viewing pictures by means of the Self-Assessment Manikin. Subjective mood ratings were also obtained by means of Visual Analog Scales. No significant effect of SD was observed on the evaluation of pleasant and unpleasant stimuli. On the contrary, SD subjects perceived the neutral pictures more negatively and showed an increase of negative mood and a decrease of subjective alertness compared to non-deprived subjects. Finally, an analysis of covariance on mean valence ratings of neutral pictures using negative mood as covariate confirmed the effect of SD. Our results indicate that sleep is involved in regulating emotional evaluation. The emotional labeling of neutral stimuli biased toward negative responses was not mediated by the increase of negative mood. This effect can be interpreted as an adaptive reaction supporting the "better safe than sorry" principle. It may also have applied implications for healthcare workers, military and law-enforcement personnel.
A MODERN TAKE
(2018)

We revisit the bias-variance tradeoff for neural networks in light of modern empirical findings. The traditional bias-variance tradeoff in machine learning suggests that as model complexity grows, variance increases. Classical bounds in statistical learning theory point to the number of parameters in a model as a measure of model complexity, which means the tradeoff would indicate that variance increases with the size of neural networks. However, we empirically find that variance due to training set sampling is roughly constant (with both width and depth) in practice. Variance caused by the non-convexity of the loss landscape is different. We find that it decreases with width and increases with depth, in our setting. We provide theoretical analysis, in a simplified setting inspired by linear models, that is consistent with our empirical findings for width. We view bias-variance as a useful lens to study generalization through and encourage further theoretical explanation from this perspective.
Visibility estimation and joint inpainting of lidar depth maps
(2016)

This paper presents a novel variational image inpainting method to solve the problem of generating, from 3-D lidar measures, a dense depth map coherent with a given color image, tackling visibility issues. When projecting the lidar point cloud onto the image plane, we generally obtain a sparse depth map, due to undersampling. Moreover, lidar and image sensor positions generally differ during acquisition, such that depth values referring to objects that are hidden from the image view point might appear with a naive projection. The proposed algorithm estimates the complete depth map, while simultaneously detecting and excluding those hidden points. It consists in a primal-dual optimization method, where a coupled total variation regularization term is included to match the depth and image gradients and a visibility indicator handles the selection of visible points. Tests with real data prove the effectiveness of the proposed strategy.
Review of Electromagnetic Techniques for Breast Cancer Detection
(2011)

Breast cancer is anticipated to be responsible for almost 40,000 deaths in the USA in 2011. The current clinical detection techniques suffer from limitations which motivated researchers to investigate alternative modalities for the early detection of breast cancer. This paper focuses on reviewing the main electromagnetic techniques for breast cancer detection. More specifically, this work reviews the cutting edge research in microwave imaging, electrical impedance tomography, diffuse optical tomography, microwave radiometry, biomagnetic detection, biopotential detection, and magnetic resonance imaging (MRI). The goal of this paper is to provide biomedical researchers with an in-depth review that includes all main electromagnetic techniques in the literature and the latest progress in each of these techniques.
Mapping CMMI Project Management Process Areas to SCRUM Practices
(2007)

Over the past years, the capability maturity model (CMM) and capability maturity model integration (CMMI) have been broadly used for assessing organizational maturity and process capability throughout the world. However, the rapid pace of change in information technology has caused increasing frustration to the heavyweight plans, specifications, and other documentation imposed by contractual inertia and maturity model compliance criteria. In light of that, agile methodologies have been adopted to tackle this challenge. The aim of our paper is to present mapping between CMMI and one of these methodologies, Scrum. It shows how Scrum addresses the Project Management Process Areas of CMMI. This is useful for organizations that have their plan-driven process based on the CMMI model and are planning to improve its processes toward agility or to help organizations to define a new project management framework based on both CMMI and Scrum practices.
DepSky: Dependable and Secure Storage in a Cloud-of-Clouds
(2013)

The increasing popularity of cloud storage services has lead companies that handle critical data to think about using these services for their storage needs. Medical record databases, large biomedical datasets, historical information about power systems and financial data are some examples of critical data that could be moved to the cloud. However, the reliability and security of data stored in the cloud still remain major concerns. In this work we present DepSky, a system that improves the availability, integrity, and confidentiality of information stored in the cloud through the encryption, encoding, and replication of the data on diverse clouds that form a cloud-of-clouds. We deployed our system using four commercial clouds and used PlanetLab to run clients accessing the service from different countries. We observed that our protocols improved the perceived availability, and in most cases, the access latency, when compared with cloud providers individually. Moreover, the monetary costs of using DepSky in this scenario is at most twice the cost of using a single cloud, which is optimal and seems to be a reasonable cost, given the benefits.
A descent modified
(2005)

In this paper, we propose a modified Polak–Ribière–Polyak (PRP) conjugate gradient method. An attractive property of the proposed method is that the direction generated by the method is always a descent direction for the objective function. This property is independent of the line search used. Moreover, if exact line search is used, the method reduces to the ordinary PRP method. Under appropriate conditions, we show that the modified PRP method with Armijo-type line search is globally convergent. We also present extensive preliminary numerical experiments to show the efficiency of the proposed method.
Secure Border Gateway Protocol (Secure-BGP)
(2000)

The Border Gateway Protocol (BGP), which is used to distribute routing information between autonomous systems (ASes), is a critical component of the Internet’s routing infrastructure. It is highly vulnerable to a variety of malicious attacks, due to the lack of a secure means of verifying the authenticity and legitimacy of BGP control traffic. This document describes a secure, scalable, deployable architecture (S-BGP) for an authorization and authentication system that addresses most of the security problems associated with BGP. The paper discusses the vulnerabilities and security requirements associated with BGP, describes the S-BGP countermeasures, and explains how they address these vulnerabilities and requirements. In addition, this paper provides a comparison of this architecture with other approaches that have been proposed, analyzes the performance implications of the proposed countermeasures, and addresses operational issues.
Empowering personalized medicine with big data and semantic web technology: Promises, challenges, and use cases
(2014)

In healthcare, big data tools and technologies have the potential to create significant value by improving outcomes while lowering costs for each individual patient. Diagnostic images, genetic test results and biometric information are increasingly generated and stored in electronic health records presenting us with challenges in data that is by nature high volume, variety and velocity, thereby necessitating novel ways to store, manage and process big data. This presents an urgent need to develop new, scalable and expandable big data infrastructure and analytical methods that can enable healthcare providers access knowledge for the individual patient, yielding better decisions and outcomes. In this paper, we briefly discuss the nature of big data and the role of semantic web and data analysis for generating “smart data” which offer actionable information that supports better decision for personalized medicine. In our view, the biggest challenge is to create a system that makes big data robust and smart for healthcare providers and patients that can lead to more effective clinical decision-making, improved health outcomes, and ultimately, managing the healthcare costs. We highlight some of the challenges in using big data and propose the need for a semantic data-driven environment to address them. We illustrate our vision with practical use cases, and discuss a path for empowering personalized medicine using big data and semantic web technology.
10 user interface elements for mobile learning application development
(2015)

Mobile learning application is a new fashionable trend. More than a thousand learning applications have been designed and developed. Therefore, the focus on mobile learning applications needs to be refined and improved in order to facilitate users' interaction and response towards the learning process. To develop an effective mobile learning application, one should start from the User Interface (UI) because an effective UI can play a role in constantly making the user focus on an object and subject. The purpose of this study is to investigate and identify the best UI elements to use in mobile learning application development. Four existing UI guidelines and 12 selected learning applications were analysed, and we identified 10 UI Elements in order to develop the next mobile learning applications. All the 10 elements are described accordingly and they have implications for those designing and implementing UI in mobile learning applications.
Automated Accessibility Testing of Mobile Apps
(2018)

It is important to make mobile apps accessible, so as not to exclude users with common disabilities such as blindness, low vision, or color blindness. Even when developers are aware of these accessibility needs, the lack of tool support makes the development and assessment of accessible apps challenging. Some accessibility properties can be checked statically, but user interface widgets are often created dynamically and are not amenable to static checking. Some accessibility checking frameworks analyze accessibility properties at runtime, but have to rely on existing thorough test suites. In this paper, we introduce the idea of using automated test generation to explore the accessibility of mobile apps. We present the MATE tool (Mobile Accessibility Testing), which automatically explores apps while applying different checks for accessibility issues related to visual impairment. For each issue, MATE generates a detailed report that supports the developer in fixing the issue. Experiments on a sample of 73 apps demonstrate that MATE detects more basic accessibility problems than static analysis, and many additional types of accessibility problems that cannot be detected statically at all. Comparison with existing accessibility testing frameworks demonstrates that the independence of an existing test suite leads to the identification of many more accessibility problems. Even when enabling Android's assistive features like contrast enhancement, MATE can still find many accessibility issues.
Examining the Testing Effect with Open-and Closed-Book Tests
(2008)

Two experiments examined the testing effect with open-book tests, in which students view notes and textbooks while taking the test, and closed-book tests, in which students take the test without viewing notes or textbooks. Subjects studied prose passages and then restudied or took an openor closed-book test. Taking either kind of test, with feedback, enhanced long-term retention relative to conditions in which subjects restudied material or took a test without feedback. Open-book testing led to better initial performance than closed-book testing, but this benefit did not persist and both types of testing produced equivalent retention on a delayed test. Subjects predicted they would recall more after repeated studying, even though testing enhanced long-term retention more than restudying. These experiments demonstrate that the testing effect occurs with both openand closedbook tests, and that subjects fail to predict the effectiveness of testing relative to studying in enhancing later recall. Copyright # 2007 John Wiley & Sons, Ltd.
The Power of Testing Memory: Basic Research and Implications for Educational Practice.
(2006)

A powerful way of improving one's memory for material is to be tested on that material. Tests enhance later retention more than additional study of the material, even when tests are given without feedback. This surprising phenomenon is called the testing effect, and although it has been studied by cognitive psychologists sporadically over the years, today there is a renewed effort to learn why testing is effective and to apply testing in educational settings. In this article, we selectively review laboratory studies that reveal the power of testing in improving retention and then turn to studies that demonstrate the basic effects in educational settings. We also consider the related concepts of dynamic testing and formative assessment as other means of using tests to improve learning. Finally, we consider some negative consequences of testing that may occur in certain circumstances, though these negative effects are often small and do not cancel out the large positive effects of testing. Frequent testing in the classroom may boost educational achievement at all levels of education.
METAMEMORY : A THEORETICAL FRAMEWORK AND NEW FINDINGS
(2006)

Although there has been excellent research by many investigators on the topic of metamemory, here we will focus on our own research program. This article will begin with a description of a theoretical framework that has evolved out of metamemory research, followed by a few remarks about our methodology, and will end with a review of our previously unpublished findings. (Our published findings will not be systematically reviewed here; instead, they will be mentioned only when necessary for continuity .)
Remembering can cause forgetting: retrieval dynamics in long-term memory.
(1994)

Three studies show that the retrieval process itself causes long-lasting forgetting. Ss studied 8 categories (e.g., Fruit). Half the members of half the categories were then repeatedly practiced through retrieval tests (e.g., Fruit Or_____). Category-cued recall of unpracticed members of practiced categories was impaired on a delayed test. Experiments 2 and 3 identified 2 significant features of this retrieval-induced forgetting: The impairment remains when output interference is controlled, suggesting a retrieval-based suppression that endures for 20 min or more, and the impairment appears restricted to high-frequency members. Low-frequency members show little impairment, even in the presence of strong, practiced competitors that might be expected to block access to those items. These findings suggest a critical role for suppression in models of retrieval inhibition and implicate the retrieval process itself in everyday forgetting.
Distributed practice in verbal recall tasks: A review and quantitative synthesis.
(2006)

The authors performed a meta-analysis of the distributed practice effect to illuminate the effects of temporal variables that have been neglected in previous reviews. This review found 839 assessments of distributed practice in 317 experiments located in 184 articles. Effects of spacing (consecutive massed presentations vs. spaced learning episodes) and lag (less spaced vs. more spaced learning episodes) were examined, as were expanding interstudy interval (ISI) effects. Analyses suggest that ISI and retention interval operate jointly to affect final-test retention; specifically, the ISI producing maximal retention increased as retention interval increased. Areas needing future research and theoretical implications are discussed.